Method and system for transforming input data streams

ABSTRACT

A system and method for processing an input data stream. An input connector module receives an input data streams. A job thread is operatively connected to the received input data stream and produces an output data stream. An output connector module supplies an output data stream.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/174,591 filed Jun. 6, 2016, and entitled “METHOD AND SYSTEM FORTRANSFORMING INPUT DATA STREAMS,” which is a continuation of U.S. patentapplication Ser. No. 14/638,700 filed Mar. 4, 2015, and entitled “METHODAND SYSTEM FOR TRANSFORMING INPUT DATA STREAMS,” issued as U.S. Pat. No.9,400,703, which is a continuation of U.S. patent application Ser. No.13/745,096, filed Jan. 18, 2013, and entitled “METHOD AND SYSTEM FORTRANSFORMING INPUT DATA STREAMS,” issued as U.S. Pat. No. 9,047,146,which is a continuation of U.S. patent application Ser. No. 13/092,771,filed Apr. 22, 2011, and entitled “METHOD AND SYSTEM FOR TRANSFORMINGINPUT DATA STREAMS,” issued as U.S. Pat. No. 8,380,830, which is acontinuation of U.S. patent application Ser. No. 12/573,352, filed Oct.5, 2009, abandoned, and entitled “METHOD AND SYSTEM FOR TRANSFORMINGINPUT DATA STREAMS,” which is a continuation of U.S. patent applicationSer. No. 11/583,369, filed Oct. 19, 2006, abandoned, and entitled“METHOD AND SYSTEM FOR TRANSFORMING INPUT DATA STREAMS”, which is acontinuation of U.S. patent application Ser. No. 10/184,430, filed Jun.28, 2002, entitled “METHOD AND SYSTEM FOR TRANSFORMING INPUT DATASTREAMS,” issued as U.S. Pat. No. 7,127,520, all of which areincorporated herein by reference in their entirety.

BACKGROUND

The field of the invention relates to data transformation, and moreparticularly, to apparatus and method for transforming an input datastream in a first data format of a plurality of first data formats to anoutput data stream in a second data format of a plurality of second dataformats.

Businesses communication has become increasingly complex. The demands ofbusiness trends such as Customer Relationship Management and SupplyChain Management combined with emerging communication technologies,which allow business partners to share information instantly, are mainlyresponsible for this communication evolution. The number of businesspartners and the means with which they collaborate (using e-mail, fax,public internet and mobile devices) are steadily increasing. Adding tothis complexity, a growing number of customers and suppliers requirethat the communication be tailored to their specific needs. In short,businesses today need to provide communication processes that areautomated and personalized. Meeting this challenge requires a newunderstanding of business communications in the age of the Internet.Thus, there is a need for better control of the complexity of businesscommunication.

BRIEF DESCRIPTION OF THE DRAWINGS

The features of the invention, which are believed to be novel, are setforth with particularity in the appended claims. The invention may bestbe understood by reference to the following description taken inconjunction with the accompanying drawings, in the several figures ofwhich like reference numerals identify like elements, and in which:

FIG. 1 is a general block diagram of one embodiment of a system fortransforming an input data stream in a first data format of a pluralityof first data formats to an output data stream in a second data formatof a plurality of second data formats;

FIG. 2 is a more detailed block diagram of one embodiment of the system;

FIG. 3 is a further block diagram of an implementation of one embodimentof the system;

FIG. 4 is a block diagram of one embodiment of a portion of the system;

FIG. 5 is a block diagram of another embodiment of a portion of thesystem;

FIG. 6 is a block diagram of yet another embodiment of a portion of thesystem;

FIG. 7 is a diagram of run-time phases of an embodiment of the system;and

FIG. 8 is block diagram of a further embodiment of a portion of thesystem.

DETAILED DESCRIPTION

While the invention is susceptible of embodiments in various forms,there is shown in the drawings and will hereinafter be described someexemplary and non-limiting embodiments, with the understanding that thepresent disclosure is to be considered an exemplification of theinvention and is not intended to limit the invention to the specificembodiments illustrated.

One embodiment of a system for transforming an input data stream in afirst data format of a plurality of first data formats to an output datastream in a second data format of a plurality of second data formats isdepicted in FIG. 1. A plurality of input connector modules 100, 102, 104receive respective input data streams 106, 108, 110. A plurality ofinput queues 112, 114 store the received input data streams 106, 108,110. A plurality of job threads 116, 118, 120, 122 are operativelyconnected to respective input queues 112, 114. Each job thread (116,118, 120, 122) in parallel with at least one other job thread (116, 118,120, 122) formatting a stored input data stream to produce an outputdata stream (124, 126, 128, 130). A plurality of output queues 132, 134respectively store the output data streams 124, 126, 128, 130 from theplurality of job threads 116, 118, 120, 122. A plurality of outputconnector modules 136, 138, 140 are operatively connected to the outputqueues 132, 134, the output connector modules 136, 138, 140 supplyingrespective output data streams (124, 126, 128, 130). It is to beunderstood that the novel system may have any number of input connectormodules 100, 102, 104, input queues 112, 114, job threads 116, 118, 120,122, output queues 132, 134, and output connector modules 136, 138, 140.Also, there is no restriction on how they may be shared and FIG. 1 isonly one example of a system configuration. Furthermore, a job threadmay be directly connected to an input connector and/or to an outputconnector (see job thread 122 and output connector 140 in FIG. 1, forexample).

FIG. 2 depicts an embodiment of the system in more detail. An input datastream 200 from a source device 202 or application (provider) isevaluated and manipulated based on the data content, transmissionprotocol and data format requirements of the receiving device 204 orapplication (consumer). Input can originate from a number of sources,refined and then multiplexed to multiple output channels. Thus,one-to-many and many-to-many processing from provider to consumer ispossible.

The input is processed according to communication rules 216, whichdefine how the content is transformed, delivered and presented to theconsumer. The communication rules 216 are applied based on matching theinput from the source device 202 to the requirements of the receivingdevice 204 of the output data stream 208.

At runtime, the input data stream 200 is described in an event parsingmodel 210 and a corresponding transformation model 212 upon which thedata transformation is based. The data stream is manipulated based onmapping rules 214 in the transformation model 212, communication rules216 in the process model 218 and the content and structure of the inputevent.

The event parsing model 210, transformation model 212, and process model218 are statically defined in a design phase and determine the globalframework for the communication process between the provider (sourcedevice 202) and the consumer (receiving device 204). The input eventparsing model 210 is defined using an event tool 220, which defines thesequences and patterns to detect in the input data stream 200. Thetransformation model 212 can correspond to the event parsing model 210or can consist of a combination of events derived from the data streamor from additional mapping rules defined at design time in a mappingtool 222. The processing rules 224 for the presentation and delivery tothe output data stream is defined in the process tool 226.

External communication rules for the processing and delivery of theinformation personalized for the consumer is derived from a matchingconsumer communication model 230 at run time. The consumer communicationmodel 230 is dynamic and need not be predefined before the informationis transformed or processed at runtime. The consumer communication model230 is applied to the processing model 218 to determine the actualcommunication rules.

The event tool 220, the mapping tool 222, and the process tool 226 occurin the design phase 232. The event parsing model 210, the transformationmodel 212, and the process model 218 form the provider schema 234. Inthe runtime phase 236 the input data stream 200 is received by an eventagent 228, which parses the input data stream 200. A transformationengine 240 effects the actual transformation of the data from one formatto another format. A process engine 242 then applies the communicationrules 216 and sends the output data stream 208 to the receiving device204.

The multi-threading system increases the performance and providessupport for parallel job execution. This system architecture also offersbetter scalability for multi-processor systems. All threads areconnected to queues and/or connectors, enabling extremely flexibleconfiguration. Several job threads can serve one or several queues andseveral input connectors can use one or several queues and job threads.

In one embodiment job threads pick up data from the queue in the sameorder as it was stored. Jobs that arrive via input connectors are storedin input queues, and job threads pick up the jobs and execute themindependently of other job threads. When an input connector has writtena job to a queue, that connector is immediately ready to receive moredata; it does not have to wait for the system to process previous jobs.After processing, jobs are stored in output queues, from where outputconnectors can pick them up and pass them on to their final destination.Thus, the use of queuing is one embodiment of the system.

The following is a more detailed description of the operation of thesystem and method for transforming an input data stream in a first dataformat of a plurality of first data formats to an output data stream ina second data format of a plurality of second data formats.

In the embodiment depicted in FIG. 3, the server 300 is the “mainengine” and is configured using a project tool 302. All configurationsare defined in the project tool 302 and then exported in two text files304, 306 for platform configuration and message configuration to theserver 300. The server 300 reads these files 304, 306 at startup andcreates and connects events 308, processes 310 and queues 312, 314according to the instructions in the files 304, 306. This embodimentfocuses on how the server 300 builds its pipelines and how it processesdata 316 from a business application 318 and provides output data 320.The system is applicable to other applications, which need to reformatdata streams. During an initiation phase the project tool 302 uses asample file 322 from the business application 318. As will be explainedbelow, the server 300 has a paring model 324 and a runtime model 326.

The system is multi-threading, but for the purpose of describing theoperation of the system, the threading model is considered to consist ofa main thread 400 and input threads, such as input thread 402 (see FIG.4). The main thread 400 is responsible for initiation. It parses allcommand line options, all driver files and all export files from theproject tool. Based on this information it creates the parsing model404. Finally it creates one input thread 402 for each input queue,starts these threads and then becomes passive. It remains passive untilit gets a signal that a user wants to terminate the server. When thisoccurs, it stops all input threads, de-allocates all resources andexits. Each input thread listens to a physical port from which it canreceive data and execute any jobs found on this port.

The parsing model 404 is created as a read-only object by the mainthread 400 at startup and cannot be changed. The parsing model 404contains all the information specified by the user in the project tool.This information is exported to the server and stored in the parsingmodel 404.

The parsing model 404 communicates with the objects in the runtime modeland provides information such as: agent information, which isinformation about which agent 406 a thread job manager 408 shall use;variable information, which is information about which variables tocreate and instantiate; message structure, which is information abouthow to structure a message (such as messages 410, 412); output action,which is how the process communicates with the parsing model 404 toreceive instructions about which actions to take (These actions mayinclude sending output to the output pipeline 414, running a script orcarrying out sorting, for example); sorting information, which isinformation about whether sorting should be done or not; output pipelineobjects information, which is information regarding how the thread jobmanager 408 creates the output pipeline 414 and makes sure that therequired objects are inserted into the pipeline 414 based on informationin the parsing model 404; events and processes information, which isinformation regarding which events 416 to detect in the data stream andwhich processes to launch when an event 416 is detected.

The runtime model contains components that are created at start-up anddynamic components that are created during runtime. The main thread 500creates the parsing model 502 and all input threads, such as inputthread 504. These components cannot be changed during the session. Allother components, events, messages and output pipeline objects, aredynamically created at runtime.

The following is a step-by-step description of an example of the flow inone embodiment of the runtime model.

-   -   1. When the server starts, the main thread 500 creates the        parsing model and all input threads 504 by using information in        the files exported from the project tool. When this is done, the        main thread becomes idle and listens only to a server shutdown        command. When this occurs, the main thread 500 is responsible        for closing all input threads 504.    -   2. Input data (from a business application, for example) is        received by a physical input 506.    -   3. A filter 508 in the input thread 504 ensures that only        relevant data is passed to an agent 510.    -   4. When the agent 510 receives the data, the collect-phase        begins. In this phase the agent 510 reads the entire input file        and then carries out the following steps for each event 512 in        the job;        -   4.1. The event 512 is identified and the data is retrieved            from it.        -   4.2. A field list is created for the event 512.        -   4.3. The retrieved script for the event 512 is run. Once            these steps have been carried out for each event 512,            sorting (if any) is performed using variables set in the            events 512 and the retrieved scripts.    -   5. The collect phase is now complete.    -   6. When the thread job manager 514 receives permission from the        global thread manager, the first event 512 is created by the        thread job manager 514. Information about how to create the        event 512 is retrieved from the parsing model 502.    -   7. The agent 510 fills the event with fields.    -   8. The event 512 creates a message 516 based on the event's        field list and the information in the parsing model 502. A        message tree is built using fields, blocks and variables. The        message 516 is then passed on to the thread job manager 514.    -   9. The thread job manager 514 runs “script before event”.    -   10. The thread job manager 514 creates a process 520 by using        information in the parsing model 502 and message 518.    -   11. The thread job manager 514 runs “script before process”.    -   12. A check is made to determine if this process should be        skipped. A skip can be forced by a rule attached to the process        520 or by executing a script function “skip ( )” in the “script        before process”.    -   13. If no skip is detected, the thread job manager 514 creates        the output pipeline 522 for the process 520. This is based on        the information in the parsing model 502. The process 520 is        then executed according to the instructions in the parsing model        502 and in the data flow. The output pipeline 522 may contain        objects, such as sort/archive 524, driver 526, physical output        528. The output pipeline 522 may be operatively connected to a        receiving device 530.    -   14. When the process 520 is finished, “script after process” is        executed.    -   15. Steps 12 to 14 are repeated for all processes 520 defined        for the event 512.    -   16. When all processes 520 are created the thread job manager        514 runs “script after event”.    -   17. Steps 9 to 16 are performed for each event 512.

In another embodiment depicted in FIG. 6 an input pipeline (input thread600) consists of a pipeline of objects that are connected through onedata channel and one message channel. The pipeline 600 always startswith a physical input object 602 and ends with a thread job manager 604.Other objects can be inserted between the physical input object 602 andthe thread job manager 604. These objects can perform various operationswith the data as long as they send it to the next object in thepipeline. Normally these objects are filters 606 that remove unwanteddata from the data channel.

Each input thread 600 consists of only one input pipeline. Its only taskis to find incoming jobs arriving at the physical input object 602 andsend jobs down to the different objects in the pipeline. Eventually, itreaches the thread job manager 604 that processes a job.

The physical input object 602 is a physical port through which incomingdata is received. It is also the start of the input thread data,pipeline. A physical port may be one of the following types: serial(receives data directly from a serial port); directory scan (scans afile system directory for files that match a file search criterion);device (listens directly to a hardware device, e.g., a parallel port);standard input (listens to standard input); TCP/IP sockets (listens to asocket for incoming data); named pipe; (listens: to a named pipe);internal (data is sent from a server output queue in the same system);netware bindery (acts as a NetWare printer); netware NDS (acts as aNetWare NDS printer).

The physical input object 602 starts to listen for incoming data. Assoon as the physical input object 602 detects an incoming job thephysical input object 602 sends the job down the input thread datapipeline byte by byte as raw data. How ports are listened to depend onthe type of port.

If a filter has been chosen for the input queue in project tool, aninput filter object 606 is inserted in the input thread data pipeline600 after the physical input object 602. If several filters have beenchosen, several filter objects are inserted in serial in the pipeline600.

A filter's task is to remove unwanted sequences or to convert sequencesin the incoming data stream. An example of removing sequences is afilter that removes PCL escape codes and just sends the actual PCLdocument data to the next object in the pipeline. An example ofconverting is a filter that receives compressed (zipped) data anduncompresses (unzips) it before sending it to the next object.

The script language makes it possible at runtime to decide what outputto produce and to which queue to send it. The script language is anevent driven procedural language.

The input thread data pipeline of the input thread 600 always ends witha thread job manager 604. Each thread job manager 604 contains an agent610. The thread job manager 604 is responsible for detecting events 608and launching and controlling events 608 and processes.

An agent 610 is the interface between the thread job manager 604 and theinput thread data pipeline and receives the incoming data. It isresponsible for detecting events and extracting fields in the raw datainput stream. There may be several different agents 610; eachspecialized for a specific type of input. For example, one agent forrecord based input from mainframes, another agent for XML data. Theagent to use is specified in the project tool. The thread job manager604 finds this information in the parsing model 612. In one embodimentthe agent 610 receives data as one page and breaks it down into a fieldlist.

The agent 610, when a job arrives and when events are found in the job,notifies the thread job manager 604. The thread job manager's main taskis to control the execution of the job (i.e., the events, scripts,sorting and processes of the job). When executing the job, the threadjob manager 604 creates events and processes and makes sure that theyare executed in the right order. When processes are executed, the threadjob manager 604 is also responsible for setting up the output pipeline616 for the process.

In general, the main task for the process is to produce output and sendit to an output pipeline. The data may be received as a messagecontaining blocks that contain fields. In this embodiment the executionis block driven, meaning that the process identifies all blocks in themessage and then communicates with the parsing model to get instructionsabout which actions to take for each block, for example, to send outputto the output pipeline, to run a script or to perform sorting. The typeof output created differs depending on the type of process used.

The following are examples of types of processes. The process “PageOUTproduces a page layout. This is by far the most complicated process andis used for creating documents for printing, faxing, PDF, web etc. Theprocess “StreamOUT” produces flat field and record based text files. Theprocess “XMLOUT” produces XML output. This is a special version of“StreamOUT”. The process “Mail OUT produces e-mail and can also attachthe result of another process to the e-mail. The process “SMSOUT”produces SMS messages that can be sent to mobile phones.

In another embodiment output sent to the output pipeline is sent as metarecords containing instructions for the device drivers. An example of ameta record is as follows: output the text “, Inc.” at position x=346and y=345 using font Arial size 10. When fields and variables are usedin the output, the process retrieves the current field or variablevalue. This means that a reference to a field or variable is neverincluded in meta records. Instead, the value of the field or variable issent. To the output pipeline objects, it is transparent if it is statictext or text from the incoming data that is being delivered. The devicedrivers convert Meta records to device specific output. The devicedrivers are part of the output pipeline.

In thread job execution the thread job manager splits all requests thatreceive and process into jobs. Each job consists of one or more eventstogether with all processes belonging to these events. The processes cansend their output to one or more output pipelines. Each of thesepipelines produce one output entity for the complete job. For example if30 invoices are received at the input pipeline and a “PageOUT” processproduces 30 invoices and sends the invoices to a spooler system, these30 invoices being sent as one print job to the spooler.

The default scope of a job is that each input file will result in onejob. However, the incoming file may be split the incoming file intoseveral smaller jobs. The smallest possible job is when the job consistsof only one event. The thread job manager (actually the thread jobmanager agent) is responsible for deciding when a job starts and ends.Normally this is straight forward since one incoming request to aphysical input object will result in one job.

There can be many reasons for dividing a large job into smaller jobs.For example, there may be one entry in the spooler system for eachprocess, for example for each invoice. In a further embodiment somesettings may be sent to the output queue. This is usually performed atthe beginning of the job, for example downloading overlay files to aprinter.

One example of an implementation of the system occurs when an externalapplication that is required to process an output job sends this job asone file to the system. When the agent receives the job and recognizesit as something that should trigger an event, the job begins. This sendssignals to the thread job manager for the job to begin and for thecollect phase 700 to begin (see FIG. 7).

The agent will now start to scan the input for fields and new events.All fields found are stored in a list that is associated with thecurrent event. If, in the parsing model, the field is designated tocreate a variable, this is done at this stage. If a new event is foundit will be added to a list of found events, and any fields found afterthis will be associated with this event. This process continues until alist of all events, with all fields, has been created. This signals anend of the collect phase 700 to the thread job manager. The Collectphase is necessary for creating this list, which in turn is used to sortthe incoming events. Information is stored in the parsing model aboutwhether or not sorting should be carried out.

The thread job manager will now pre-process all events and processesbelonging to the job in a pre-process phase 702. During the pre-processphase 702 the whole job is executed, but without sending anything to theoutput pipeline. The pre-process phase 702 is used, for example, tocalculate the number of pages and where page breaks occur and todetermine which resources are to be used. A resource may, for example,be an overlay that should be sent to a printer. It is also possible tocancel the job, that is undo everything that has been done in the joband skip the rest of the input. This can be done conditionally, based oninput field values, in scripts. Event and process execution is carriedout in, the pre-process phase 702 in the following order:

-   -   1 The first event in the event list is pre-processed first, then        all the processes for this event.    -   2 The next event in the event list, together with its processes,        is preprocessed.    -   3 This continues until all the events in the list have been        pre-processed.

Note that this is the order after events have been sorted. Before andafter each event and process a script is run. In this script, theprocess can conditionally be skipped.

Now the thread job manager has stored all information needed from thepre-process phase 702 and can execute the events and processes in aprocess phase 704. First, it performs a rollback on everything. Forexample, variables are restored to their values before the pre-processphase 702 and ODBC operations that have been executed in a transactionare rolled-back. Next it sends any resources (for example, overlays)that were found during the pre-process phase 702 to the output pipeline.The events and processes are executed in the process phase 704 in thesame order as in the pre-process phase 702. The difference is that thistime the output is actually sent to the output pipeline. After the lastprocess is executed, the job is complete. The thread job managerreleases all resources that were temporarily assigned.

In FIG. 8 the output pipeline 800 consists of a pipeline of objects thatare connected through one data channel and one message channel. Thepipeline 800 always starts with a process 802 and ends with a physicaloutput object 804. Between the process 802 and the physical outputobject 804 other objects may be inserted. These objects may be used toperform various operations with the data and then pass the data on tothe next object in the pipeline 800. Examples of operations that may beperformed in various embodiments are sorting, or splitting the pipelineinto two branches (such as sorting object 806). Also one of the objectsmay be a device driver 808 that converts the meta data into formatteddata.

The physical output object 804 always points to a physical destination,such as receiving device 810. This can, for example, be a printer or ane-mail server. The physical output object 804 is responsible for theactual delivery of the output data to its final destination.

Different objects may be included in the pipeline 800 depending oninformation in the parsing model. The thread job manager creates theoutput pipeline 800 and ensures that the required objects are insertedin the pipeline 800. The thread job manager also connects the pipeline800 to the process 802.

In one embodiment the following rules may apply to all output pipelinesin the system: Each physical output object corresponds to one, and onlyone, queue as defined in the parsing model. There may only be onepipeline for each physical output object. The physical output object fora pipeline is always the same throughout an entire job. The pipeline isalways connected to one process at a time. These rules imply that outputfrom different processes in the same job, that use the same physicaloutput object, will be kept together, that is, delivered as one unit tothe final destination, for example a spooler system.

In the data channel, the process sends meta records down the pipeline.If there is a device driver in the pipeline, it reformats the metarecord according to the format expected by the destination. Eventuallythe information reaches the physical output object, which sends it to aphysical destination, for example, a spooler system or a file. Themessage channel is used by the thread job manager to send messages tonotify the objects in the pipeline when certain events occur.

Output processors or objects may be inserted anywhere in the pipeline.These processors may change the data that is sent through the datachannel. It is also possible to use a pipeline without any outputprocessors, that is a pipeline with just a device driver and a physicaloutput object.

Thus in general terms the present system (and the corresponding method)is for transforming an input data stream in a first data format of aplurality of first data formats to an output data stream in a seconddata format of a plurality of second data formats. A plurality of inputconnector modules receive respective input data streams and at least oneinput queue stores the received input data streams. A plurality of jobthreads is operatively connected to the at least one input queue, eachjob thread, in parallel with at least one other job thread, formatting astored input data stream to produce an output data stream. At least oneoutput queue respectively stores the output data streams from theplurality of job threads. A plurality of output connector modules isoperatively connected to the at least one output queue, the outputconnector modules supplying respective output data streams.

In an embodiment each of the job threads has at least one event agentassociated with at least one parsing model, the event agent having aninput port that receives an input data stream, and having an outputport. At least one transformation engine is associated with at least onetransformation model, the transformation engine having an input portoperatively connected to the output port of the event agent. At leastone process engine is associated with at least one process model, theprocess engine having an input port operatively connected to the outputport of the transformation engine, and having an output port forsupplying an output data stream. The transformation model has mappingrules for manipulating the input data stream, and the process model hascommunication rules for formatting the output data stream.

In another embodiment the at least one input queue may be shared betweenthe input connector modules and the job threads, and the at least oneoutput queue may be shared between the job threads and the outputconnectors. The job threads may receive input data streams in the orderin which the input data streams are stored in the at least one inputqueue. In general, the job threads receive input data streams from theat least one input queue, format the input data streams into output datastreams, and store the output data streams in the at least one outputqueue, independent of one another and in parallel.

It is to be understood, of course, that the invention in variousembodiments can be implemented in hardware, software, or in combinationsof hardware and software.

The invention is not limited to the particular details of the apparatusand method depicted, and other modifications and applications arecontemplated. Certain other changes may be made in the above-describedapparatus and method without departing from the true spirit and scope ofthe invention herein involved. It is intended, therefore, that thesubject matter in the above depiction shall be interpreted asillustrative and not illuminating sense.

What is claimed is:
 1. A system for processing input data streamscomprising: an input connector; an output connector; a processing systemcoupled to the input connector and the output connector, the processingsystem having a non-transitory computer readable medium comprisinginstructions to: read an electronic input data stream of data receivedat the input connector; in a job thread, detect patterns in the inputdata stream to identify events; in the same job thread, create a messagefor each event, the message containing data associated with the eventaccording to a generic data structure corresponding to the event; in thesame job thread, execute a process configured to generate a meta recordbased on the message and send the meta record to an output pipeline; andprovide an output data stream to a destination from the output pipelinevia the output connector, wherein at least a portion of the output datastream contains data from the message converted according to the metarecord.
 2. The system of claim 1, wherein the output data streamcomprises data not contained in the data associated with the event. 3.The system of claim 1; wherein the processing system is coupled to theinput connector via an input queue the processing system is coupled tothe output connector via an output queue.
 4. The system of claim 3,wherein the input connector and output connector are physicalconnectors.
 5. The system of claim 1, wherein the instructions arefurther for splitting the data received at the input connector into aplurality of jobs, wherein the job thread corresponds to one of theplurality of jobs.
 6. The system of claim 1, wherein the job threadincludes a plurality of job threads, each of the plurality of jobthreads processing a portion of the identified events.
 7. A method forprocessing a data stream in a network environment comprising: reading anelectronic input data stream of data received at an input connector; ina job thread on a process system, detecting patterns in the input datastream to identify events; in the same job thread, creating a messagefor each event, the message containing data associated with the eventaccording to a generic data structure corresponding to the event; in thesame job thread, executing a process configured to generate a metarecord based on the message and send the meta record to an outputpipeline; and providing an output data stream to a destination from theoutput pipeline via an output connector, wherein at least a portion ofthe output data stream contains data from the message convertedaccording to the meta record.
 8. The method of claim 7, wherein theoutput data stream comprises data not contained in the data associatedwith the event.
 9. The method of claim 7, wherein the processing systemis coupled to the input connector via an input queue, and the processingsystem is coupled to the output connector via an output queue.
 10. Themethod of claim 9, wherein the input connector and output connector arephysical connectors.
 11. The method of claim 7, wherein the methodfurther comprises splitting the data received at the input connectorinto a plurality of jobs, wherein the job thread corresponds to one ofthe plurality of jobs.
 12. The method of claim 7, wherein the job threadincludes a plurality of job threads, each of the plurality of jobthreads processing a portion of the identified events.
 13. Anon-transitory computer readable medium comprising a set of computerreadable instructions for: reading an electronic input data stream ofdata received at an input connector; in a job thread on a processingsystem, detecting patterns in the input data stream to identify events;in the same job thread, creating a message for each event, the messagecontaining data associated with the event according to a generic datastructure corresponding to the event; in the same job thread, executinga process configured to generate a meta record based on the message andsend the meta record to an output pipeline; and providing an output datastream to a destination from the output pipeline via an outputconnector, wherein at least a portion of the output data stream containsdata from the message converted according to the meta record.
 14. Thenon-transitory computer readable medium of claim 13, wherein the outputdata stream comprises data not contained in the data associated with theevent.
 15. The non-transitory computer readable medium of claim 13,wherein the processing system is coupled to the input connector via aninput queue, and the processing system is coupled to the outputconnector via an output queue.
 16. The non-transitory computer readablemedium of claim 15, wherein the input connector and output connector arephysical connectors.
 17. The non-transitory computer readable medium ofclaim 13, wherein the instructions are further for splitting the datareceived at the input connector into a plurality of jobs, wherein thejob thread corresponds to one of the plurality of jobs.
 18. Thenon-transitory computer readable medium of claim 13, wherein the jobthread includes a plurality of job threads, each of the plurality of jobthreads processing a portion of the identified events.